class TrainingConfig(object):
    train_epochs = 10
    epochs_per_eval = 1
    tensorboard_images_max_outputs = 6
    batch_size = 8
    learning_rate_policy = 'poly' # choices=['poly', 'piecewise']
    max_iter = 30000
    base_architecture = 'resnet_v2_50' # choices=['resnet_v2_50', 'resnet_v2_101']
    initial_learning_rate = 7e-3
    end_learning_rate = 5e-5
    initial_global_step = 0
    weight_decay = 1e-4 # regulization
    pre_trained_model = './resnet_v2_50_2017_04_14/resnet_v2_50.ckpt'
    model_type = 1 # 0 for ce, 1 for bfl, 2 for original
    model_dir = './model_original'
    if model_type == 1:
        model_dir = './model_bfl'
    elif model_type == 0:
        model_dir = './model_ce'
    else:
        pre_trained_model = './resnet_v2_101_2017_04_14/resnet_v2_101.ckpt'
        base_architecture = 'resnet_v2_101'  # choices=['resnet_v2_50', 'resnet_v2_101']
        initial_learning_rate = 7e-5
        batch_size = 2


